What type of study is this?

This is a Animal Study study (also classified as: Observational).

September 10, 2025

Automatic detection and range estimation using a passive acoustic monitoring sensor network in a stratified and range-dependent waveguide

Key Points

The deep learning model successfully detects and estimates range from received acoustic signals.
Simulation and experimental marine data validate the detection and ranging method, demonstrating its efficacy.
Challenges include addressing the range dependence and stratified properties of the environment in real-time.
Leveraging a distributed sensor network enhances the performance of the acoustic monitoring approach.

Abstract

In shallow-water environments, the sound generated by low-frequency sources interacts significantly with the sea surface and seabed, inducing a strong dependence on source range in the received pressure field due to modal dispersion. As a result, these signals are ideal for range-based localization. However, in complex environments that are range dependent or that exhibit stratified water column and/or seabed properties, inverting the received signal for range in real time is nontrivial. Here, we develop a deep-learning approach to detect and range received signals that accounts for the distributed nature of the deployed sensor network and the stratified nature of the environment which varies with spatial position. The model is trained using simulated data generated based on the parameter distributions observed in the target environment. The detection and ranging method is validated using both simulated and experimental marine data. Work supported by NDSEG and ONR.

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